To `repair' the images, inpainting techniques must be used. Image inpainting, also known as gap-filling, aims to fill the corrupted pixels in an image with values that resemble the original pixel values as closely as possible. Inpainting for corrupted image data is an active area of research and many methods have been introduced. For instance, many deterministic methods have been proposed, that use higher order differential equations [1]}, [2]}, [3]}. Moreover, recent advances in deep learning have shown promising results in image inpainting. [4]} introduced the celebrated U-Net, which was originally used for biomedical image segmentation, but can be trained to perform image inpainting, and [5]} introduced Partial Convolutions (PartialConv), which is a modification to the classical convolutional layer to make it suitable for inpainting.
